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Introduction to dataset preprocessing

In the actual world, data is frequently incomplete: it lacks attribute values, specific attributes of relevance are missing, or it simply contains aggregate data. Errors or outliers make the data noisy. Inconsistent: having inconsistencies in codes or names. The Keras dataset pre-processing utilities assist us in converting raw disc data to a tf. data file. A dataset is a collection of data that may be used to train a model. In this topic, we are going to learn about dataset preprocessing.

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Why use dataset pre-processing?

By pre-processing data, we can:

Improve the accuracy of our database. We remove any values that are wrong or missing as a consequence of human error or problems.

Consistency should be improved. The accuracy of the results is harmed when there are data discrepancies or duplicates.

Make the database as complete as possible. If necessary, we can fill up the missing properties.

The data should be smooth. We make it easier to use and interpret this manner.

We have few Dataset pre-processing Utilities:



Time series

Importing datasets pre-processing

Steps for Importing a dataset in Python:

Importing appropriate Libraries

import matplotlib.pyplot as mpt

    Import Datasets

    The datasets are in chúng tôi format. A CSV file is a plain text file that consists of tabular data. A data record is represented by each line in the file.

    dataset = pd.read_csv('Data.csv')

    We’ll use pandas’ iloc (used to fix indexes for selection) to read the columns, which has two parameters: [row selection, column selection].

    x = Dataset.iloc[:, :-1].values

    Let’s have the following incomplete datasets

    Name Pay Managers

    AAA 40000 Yes

    BBB 90000

    60000 No



    DDD 30000 Yes

    As we can see few missing cells are in the table. To fill these we need to follow a few steps:

    from sklearn.preprocessing import Imputer

    Next By importing a class

    // Using Missing Indicator to fit transform.

    Splitting a dataset by training and test set.

    Installing a library:

    from sklearn.cross_validation import train_test_split

    A_train, A_test, B_train, B_test = train_test_split(X, Y, test_size = 0.2)

    Feature Scaling

    A_test = scale_A.transform(A_test)

    Example #1

    names = [‘sno’, ‘sname’, ‘age’, ‘Type’, ‘diagnosis’, ‘in’, ‘out’, ‘consultant’, ‘class’] X = array[:, 0:8] Y = array[:, 8]


    All of the data preprocessing procedures are combined in the above code.


    Feature datasets pre-processing

    Outliers are removed during pre-processing, and the features are scaled to an equivalent range.

    Steps Involved in Data Pre-processing

    Data cleaning: Data can contain a lot of useless and missing information. Data cleaning is carried out to handle this component. It entails dealing with missing data, noisy data, and so on. The purpose of data cleaning is to give machine learning simple, full, and unambiguous collections of examples.

    a) Missing Data: This occurs when some data in the data is missing. It can be explored in many ways.

    Here are a few examples:

    Ignore the tuples: This method is only appropriate when the dataset is huge and many values are missing within a tuple.

    Fill in the blanks: There are several options for completing this challenge. You have the option of manually filling the missing values, using the attribute mean, or using the most likely value.

    b) Noisy Data: Data with a lot of noise

    The term “noise” refers to a great volume of additional worthless data.

    Duplicates or semi-duplicates of data records; data segments with no value for certain research; and needless information fields for each of the variables are examples of this.

    Method of Binning:

    This approach smoothes data that has been sorted. The data is divided into equal-sized parts, and the process is completed using a variety of approaches.


    Regression analysis aids in determining which variables do have an impact. To smooth massive amounts of data, use regression analysis. This will help to focus on the most important qualities rather than trying to examine a large number of variables.

    Clustering: In this method, needed data is grouped in a cluster. Outliers may go unnoticed, or they may fall outside of clusters.

      Data Transformation

      We’ve already started modifying our data with data cleaning, but data transformation will start the process of transforming the data into the right format(s) for analysis and other downstream operations. This usually occurs in one or more of the following situations:



      Selection of features


      The creation of a concept hierarchy

        Data Reduction:

        Data mining is a strategy for dealing with large amounts of data. When dealing with bigger amounts of data, analysis faces quite a complication. We employ a data reduction technique to overcome this problem. Its goal is to improve storage efficiency and reduce analysis expenses. Data reduction not only simplifies and improves analysis but also reduces data storage.

        The following are the steps involved in data reduction:

        Attribute selection: Like discretization, can help us fit the data into smaller groups. It essentially combines tags or traits, such as male/female and manager, to create a male manager/female manager.

        Reduced quantity: This will aid data storage and transmission. A regression model, for example, can be used to employ only the data and variables that are relevant to the investigation at hand.

        Reduced dimensionality: This, too, helps to improve analysis and downstream processes by reducing the amount of data used. Pattern recognition is used by algorithms like K-nearest neighbors to merge similar data and make it more useful.

        Conclusion – dataset preprocessing

        Therefore, coming to end, we have seen Dataset processing techniques and their libraries in detail. The data set should be organized in such a way that it can run many Machines Learning and Deep Learning algorithms in parallel and choose the best one.

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        This is a guide to dataset preprocessing. Here we discuss the Dataset processing techniques and their libraries in detail. You may also have a look at the following articles to learn more –

        You're reading Learn The Dataset Processing Techniques

        Learn The Different Test Techniques In Detail

        Introduction to Test techniques

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        List of Test techniques

        There are various techniques available; each has its own strengths and weakness. Each technique is good at finding particular types of defects and relatively poor at finding other types of defects. In this section, we are going to discuss the various techniques.

        1. Static testing techniques 2. Specification-based test techniques

        all Specification-based techniques have the common characteristics that they are based on the model of some aspect of the specification, enabling the cases to be derived systematically. There are 4 sub-specification-based techniques which are as follows

        Equivalence partitioning: It is a specification-based technique in which test cases are designed to execute representatives from equivalence partition. In principle, cases are designed to cover each partition at least once.

        Boundary value analysis: It is a technique in which cases are designed based on the boundary value. Boundary value is an input value or output value which is on the edge of an equivalence partition or at the smallest incremental distance on either side of an edge. For example, minimum and maximum value.

        Decision table testing: It is a technique in which cases are designed to execute the combination of inputs and causes shown in a decision table.

        State transition testing: It is a technique in which cases are designed to execute valid and invalid state transitions.

        3. Structure-based testing

        Test coverage: It is a degree that is expressed as a percentage to which a specified coverage item has been exercised by a test suite.

        Statement coverage: It is a percentage of executable statements that the test suite has exercised.

        Decision Coverage: It is a percentage of decision outcomes that a test suite has exercised. 100% decision coverage implies both 100% branch coverage and 100% statement coverage.

        Branch coverage: It is a percentage of the branches that the test suite has exercised. 100% branch coverage implies both 100% decision coverage and 100% statement coverage.

        4. Experience-based testing

        The experience-based technique is a procedure to derive and select the cases based on the experience and knowledge of the tester. All experience-based have the common characteristics that they are based on human experience and knowledge, both of the system itself and likely defects. Cases are derived less systematically but may be more effective. The experience of both technical people and business people is a key factor in an experience-based technique.


        The most important thing to understand here is that the best technique is no single testing, as each technique is good at finding one specific class of the defect. also, using just a single technique will help ensure that any defects of that particular class are found. It may also help to ensure that any defects of other classes are missed. So using a variety of techniques will help you ensure that a variety of defects are found and will result in more effective testing. Therefore it is most often used to statistically test the source code.

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        Fix Facetime “The Server Encountered An Error Processing Registration” Error

        Facetime is one of the most reliable and intuitive video calling applications in the world, which makes it a real pity Apple keeps it to itself. Of course, no software is perfect, and now and then users can run into the “server encountered an error processing registration” error. Usually at the least opportune time!

        What Does This Error Mean?

        For a somewhat cryptic error message, the meaning of it is quite simple. FaceTime is trying to log you into the service, but something is going wrong in the process. This is especially frustrating when you seem to be doing everything correctly. Using an Apple ID that works for everything else, but just not on FaceTime.

        Table of Contents

        Sadly, this one error can have multiple causes, which means that you’ll have to rely a little on trial and error to fix it. We’re going to go through the various possible fixes from the easiest to the most effort.

        The below tips and tricks are aimed at Mac users, if you’re having trouble on an iOS device, start here.

        Is It Really You?

        Don’t assume that in this transaction between your computer and the remote server it’s necessarily your computer that’s the culprit. Try to check social media or official Apple channels for any indications that there’s a service outage or some other general problem. 

        If other people are also having similar issues at the same time as you are, then it’s worth waiting a while to see if the issue resolves itself.

        Update, Update, Update Restart Your Mac & Your Net Connection Or Try a Different Internet Connection

        Do a cold reboot of your Mac and reset your router or another device that’s providing an internet connection. Just in case something weird is going on with your internet connection.

        If an internet connection reset doesn’t work, that doesn’t mean your internet connection isn’t the problem. Try using the device which is giving you the error on another internet connection, such as a temporary hotspot on your smartphone. 

        If switching connections entirely doesn’t do the trick, and it’s not a problem anyone else seems to be having, then the problem may be local to your device. To nail this down though, we need one more diagnostic step.

        Try a Different Device

        This might not be possible for everyone, but if you have another Mac, iPad, or iPhone with Facetime on it, try using that and see if the problem persists. If it doesn’t then we can be pretty sure it’s a local problem with your Mac. 

        If it follows you around from one device to the next, you’ll have to wait out a server-side problem or get in touch with Apple Support to check if there’s something wrong with your Apple ID.

        Log Out & In Again

        If you’ve determined that the problem only happens on your Mac, then the next step is to log out of your Apple ID in FaceTime and then log back in again. This is pretty easy to do:

        You’ll then see this sign-in page, where you can try to log in again.

        Check The Date & Time

        Is your Mac’s date and time correct? Simply go to the Date and Time utility (it’s fastest via Spotlight Search) and check that the date and time are correct.

        You should also check to see if the automatic date and time option is checked, so your Mac will pull the correct date and time from the internet whenever it connects.

        Older Methods That Aren’t Supported

        If you’ve been searching for a fix to this “server encountered an error processing registration” issue, you’ve probably run across several guides and articles from between 2010 and 2024 detailing various ways to solve the issue. While some of that information is still valid, there are two which don’t seem to be relevant anymore.

        The first has to do with editing the macOS “hosts” file. While there are various reasons to mess with this file, we can’t find any evidence that this specific FaceTime error has anything to do with the macOS hosts file, so it’s not something we recommend you mess with.

        Learning Different Techniques Of Anomaly Detection

        This article was published as a part of the Data Science Blogathon.


        As a data scientist, in many cases of fraud detection in the bank for a transaction, Smart meters anomaly detection,

        Have you ever thought about the bank where you make several transactions and how the bank helps you by identifying fraud?

        Someone logins to your account, and a message is sent to you immediately after they notice suspicious activity at a place to confirm if it’s you or someone else.

        What is Anomaly Detection?

        Suppose we own a otice the font end data errors, even when our company supplies the same service, but the sales are declining. Here come the errors, which are termed anomalies or outliers.

        Let’s take an example that will further clarify what an means.

        Source: Canvas

         Here in this example, a bird is an outlier or noise.

        Have you ever thought about the bank where you make several transactions, How the bank helps you by identifying fraud detection?

        If the bank manager notices unusual behavior in your accounts, it can block the card. For example, spending a lot of amount in one day or another sum amount on another day will send a message of alert or will block your card as it’s not related to how you were spending previously.

        Two AI firms detect an anomaly inside the bank. One is Fedzai’s detection firm, and another one by Ayasdi’s solution.

        Let’s take another example of shopping e end of the month, the shopkeeper puts certain items on sale and offers you a scheme where you can buy two at less rate.

        Now how do we describe the sales data compared to the start-of-month data? Do sale data validate data concerning monthly sales at the start of selling? It’s not vali

        Outliers are “something I should remove from the dataset so that it doesn’t skew the model I’m building,” typically because they suspect that the data in question is flawed and that the model they want to construct shouldn’t need to take into account.

        Outliers are most commonly caused by:

        Intentional (dummy outliers created to test detection methods)

        Data processing errors (data manipulation or data set unintended mutations)

        Sampling errors (extracting or mixing data from wrong or various sources)

        Natural (not an error, novelties in the data)

        An actual data point significantly outside a distribution’s mean or median is an outlier.

        An anomaly is a false data point made by a different process than the rest of the data.

        If you construct a linear regression model, it is less likely that the model generated points far from the regression line. The likelihood of the data is another name for it.

        Outliers are data points with a low likelihood, according to your model. They are identical from the perspective of modeling.

        For instance, you could construct a model that describes a trend in the data and then actively looks for existing or new values with a very low likelihood. When people say “anomalies,” they mean these things. The anomaly detection of one person is the outlier of another!

        Extreme values in your data series are called outliers. They are questionable. One student can be much more brilliant than other students in the same class, and it is possible.

        However, anomalies are unquestionably errors. For example, one million degrees outside, or the air temperature won’t stay the same for two weeks. As a result, you disregard this data.

        An outlier is a valid data point, and can’t be ignored or removed, whereas noise is garbage that needs removal. Let’s take another example to understand noise.

        Suppose you wanted to take the average salary of employees and in data added the pay of Ratan Tata or Bill Gate, all the employer’s salary averages will show an increase which is incorrect data.


        2. Uni-variate – Uni-variate a variable with different values in the dataset.

        3. Multi-variate – It is defined by the dataset by having more than one variable with a different set of values.

        We will now use various techniques which will help us to find outliers.

        Anomaly Detection by Scikit-learn

        We will import the required library and read our data.

        import seaborn as sns import pandas as pd titanic=pd.read_csv('titanic.csv') titanic.head()


        We can see in the image many null values. We will fill the null values with mode.

        titanic['age'].fillna(titanic['age'].mode()[0], inplace=True) titanic['cabin'].fillna(titanic['cabin'].mode()[0], inplace=True) titanic['boat'].fillna(titanic['boat'].mode()[0], inplace=True) titanic['body'].fillna(titanic['body'].mode()[0], inplace=True) titanic['sex'].fillna(titanic['sex'].mode()[0], inplace=True) titanic['survived'].fillna(titanic['survived'].mode()[0], inplace=True) titanic['home.dest'].fillna(titanic['home.dest'].mode()[0], inplace=True)

        Let’s see our data in more detail. When we look at our data in statistics, we prefer to know its distribution types, whether binomial or other distributions.

        titanic['age'].plot.hist( bins = 50, title = "Histogram of the age" )

        This distribution is Gaussian distribution and is often called a normal distribution.

        Mean and Standard Deviation are considered the two parameters. With the change in mean values, the distribution curve changes to left or right depending on the mean values.

        Standard Normal distribution means mean(μ = 0) and standard deviation (σ) is one. To know the probability Z-table is already available.


        We can calculate Z – Scores by the given formula where x is a random variable, μ is the mean, and σ is the standard deviation.

        Why do we need Z-Scores to be calculated?

        It helps to know how a single or individual value lies in the entire distribution.

        For example, if the maths subject scores mean is given to us 82, the standard deviation σ is 4. We have a value of x as 75. Now Z-Scores will be calculated as 82-75/4 = 1.75. It shows the value 75 with a z-score of 1.75 lies below the mean. It helps to determine whether values are higher, lower, or equal to the mean and how far.

        Now, we will calculate Z-Score in python and look at outliers.

        We imported Z-Scores from Scipy. We calculated Z-Score and then filtered the data by applying lambda. It gives us the number of outliers ranging from the age of 66 to 80.

        from scipy.stats import zscore titanic["age_zscore"] = zscore(titanic["age"]) titanic["outlier"] = titanic["age_zscore"].apply( lambda x: x = 2.8 ) titanic[titanic["outlier"]]

        We will now look at another method based on clustering called Density-based spatial clustering of applications with noise (DBSCAN).


        As the name indicates, the outliers detection is on clustering. In this method, we calculate the distance between points.

        Let’s continue our titanic data and plot a graph between fare and age. We made a scatter graph between age and fare variables. We found three dots far away from the others.

        Before we proceed further, we will normalize our data variables.

        There are many ways to make our data normalize. We can import standard scaler by sklearn or min max scaler.

        titanic['fare'].fillna(titanic['fare'].mean(), inplace=True) from sklearn.preprocessing import StandardScaler scale = StandardScaler() fage = scale.fit_transform(fage) fage = pd.DataFrame(fage, columns = ["age", "fare"]) fage.plot.scatter(x = "age", y = "fare")

        We used Standard Scaler to make our data normal and plotted a scatter graph.

        Now we will import DBSCAN to give points to the clusters. If it fails, it will show -1.

        from sklearn.cluster import DBSCAN outlier = DBSCAN( eps = 0.5, metric="euclidean", min_samples = 3, n_jobs = -1) clusters = outlier.fit_predict(fage) clusters  array([0, 1, 1, ..., 1, 1, 1])

        Now we have the results, but how do we check which value is min, max and whether we have -1 values? We will use the arg min value to check the smallest value in the cluster.

        value=-1 index = clusters.argmin() print(" The element is at ", index) small_num = np.min(clusters) print("The small number is : " , small_num) print(np.where(clusters == small_num)) The element is at: 14 The small number is : -1 (array([ 14, 50, 66, 94, 285, 286], dtype=int64),)

        We can see from the result six values which are -1.

        Lets now plot a scatter graph.

        from matplotlib import cm c = cm.get_cmap('magma_r') fage.plot.scatter( x = "age", y = "fare", c = clusters, cmap = c, colorbar = True )

        The above methods we applied are on uni-variate outliers.

        For Multi-variates outliers detections, we need to understand the multi-variate outliers.

        For example, we take Car readings. We have seen two reading meters one for the odometer, which records or measures the speed at which the vehicle is moving, and the second is the rpm reading which records the number of rotations made by the car wheel per minute.

        Suppose the odometer shows in the range of 0-60 mph and rpm in 0-750. We assume that all the values which come should correlate with each other. If the odometer shows a 50 speed and the rpm shows 0 intakes, readings are incorrect. If the odometer shows a value more than zero, that means the car was moving, so the rpm should have higher values, but in our case, it shows a 0 value. i.e., Multi-variate outliers.

        Mahalanobis Distance Method

        In DBSCAN, we used euclidean distance metrics, but in this case, we are talking about the Mahalanobis distance method. We can also use Mahalanobis distance with DBSCAN.

        DBSCAN(eps=0.5, min_samples=3, metric='mahalanobis', metric_params={'V':np.cov(X)}, algorithm='brute', leaf_size=30, n_jobs=-1)

        Why is Euclidean unfit for entities cor-related to each other? Euclidean distance cannot find or will give incorrect data on how close are the two points.

        Mahalanobis method uses the distance between points and distribution that is clean data. Euclidean distance is often between two points, and its z-score is calculated by x minus mean and divided by standard deviation. In Mahalanobis, the z-score is x minus the mean divided by the covariance matrix.

        Therefore, what effect does dividing by the covariance matrix have? The covariance values will be high if the variables in your dataset are highly correlated.

        Similarly, if the covariance values are low, the distance is not significantly reduced if the data are not correlated. It does so well that it addresses both the scale and correlation of the variables issues.


        df = pd.read_csv('caret.csv').iloc[:, [0,4,6]] df.head()

        We defined the function distance as x= None, data= None, and Covariance = None. Inside the function, we took the mean of data and used the covariance value of the value there. Otherwise, we will calculate the covariance matrix. T stands for transpose.

        For example, if the array size is five or six and you want it to be in two variables, then we need to transpose the matrix.

        np.random.multivariate_normal(mean, cov, size = 5) array([[ 0.0509196, 0.536808 ], [ 0.1081547, 0.9308906], [ 0.4545248, 1.4000731], [ 0.9803848, 0.9660610], [ 0.8079491 , 0.9687909]]) np.random.multivariate_normal(mean, cov, size = 5).T array([[ 0.0586423, 0.8538419, 0.2910855, 5.3047358, 0.5449706], [ 0.6819089, 0.8020285, 0.7109037, 0.9969768, -0.7155739]])

        We used sp.linalg, which is Linear algebra and has different functions to be performed on linear algebra. It has the inv function for the inversion of the matrix. NumPy dot as means for the multiplication of the matrix.

        import scipy as sp def distance(x=None, data=None, cov=None): x_m = x - np.mean(data) if not cov: cov = np.cov(data.values.T) inv_cov = sp.linalg.inv(cov) left =, inv_cov) m_distance =, x_m.T) return m_distance.diagonal() df_g= df[['carat', 'depth', 'price']].head(50) df_g['m_distance'] = distance(x=df_g, data=df[['carat', 'depth', 'price']]) df_g.head() B. Tukey’s method for outlier detection

        Tukey method is also often called Box and Whisker or Box plot method.

        Tukey method utilizes the Upper and lower range.

        Upper range = 75th Percentile -k*IQR

        Lower range = 25th Percentile + k* IQR

        Let us see our Titanic data with age variable using a box plot.


        We can see in the image the box blot created by Seaborn shows many dots between the age of 55 and 80 are outliers not within the quartiles. We will detect lower and upper range by making a function outliers_detect.

        def outliers_detect(x, k = 1.5): x = np.array(x).copy().astype(float) first = np.quantile(x, .25) third = np.quantile(x, .75) # IQR calculation iqr = third - first #Upper range and lower range lower = first - (k * iqr) upper = third + (k * iqr) return lower, upper outliers_detect(titanic['age'], k = 1.5) (2.5, 54.5) Detection by PyCaret

        We will be using the same dataset for detection by PyCaret.

        from pycaret.anomaly import * setup_anomaly_data = setup(df)

        Pycaret is an open-source machine learning which uses an unsupervised learning model to detect outliers. It has a get_data method for using the dataset in pycaret itself, set_up for preprocessing task before detection, usually takes data frame but also has many other features like ignore_features, etc.

        Other methods create_model for using an algorithm. We will first use Isolation Forest.

        ifor = create_model("iforest") plot_model(ifor) ifor_predictions = predict_model(ifor, data = df) print(ifor_predictions) ifor_anomaly = ifor_predictions[ifor_predictions["Anomaly"] == 1] print(ifor_anomaly.head()) print(ifor_anomaly.shape)

        Anomaly 1 indicates outliers, and Anomaly 0 shows no outliers.

        The yellow color here indicates outliers.

        Now let us see another algorithm, K Nearest Neighbors (KNN)

        knn = create_model("knn") plot_model(knn) knn_pred = predict_model(knn, data = df) print(knn_pred) knn_anomaly = knn_pred[knn_pred["Anomaly"] == 1] knn_anomaly.head() knn_anomaly.shape

        Now we will use a clustering algorithm.

        clus = create_model("cluster") plot_model(clus) clus_pred = predict_model(clus, data = df) print(clus_pred) clus_anomaly = clus_predictions[clus_pred["Anomaly"] == 1] print(clus_anomaly.head()) clus_anomaly.shape Anomaly Detection by PyOD

        PyOD is a python library for the detection of outliers in multivariate data. It is good both for supervised and unsupervised learning.

        from pyod.models.iforest import IForest from chúng tôi import KNN

        We imported the library and algorithm.

        from chúng tôi import generate_data from chúng tôi import evaluate_print from pyod.utils.example import visualize train= 300 test=100 contaminate = 0.1 X_train, X_test, y_train, y_test = generate_data(n_train=train, n_test=test, n_features=2,contamination=contaminate,random_state=42) cname_alg = 'KNN' # the name of the algorithm is K Nearest Neighbors c = KNN() #Fit the algorithm y_trainpred = c.labels_ y_trainscores = c.decision_scores_ y_testpred = c.predict(X_test) y_testscores = c.decision_function(X_test) print("Training Data:") evaluate_print(cname_alg, y_train, y_train_scores) print("Test Data:") evaluate_print(cname_alg, y_test, y_test_scores) visualize(cname_alg, X_train, y_train, X_test, y_test, y_trainpred,y_testpred, show_figure=True, save_figure=True)

        We will use the IForest algorithm.

        fname_alg = 'IForest' # the name of the algorithm is K Nearest Neighbors f = IForest() #Fit the algorithm y_train_pred = c.labels_ y_train_scores = c.decision_scores_ y_test_pred = c.predict(X_test) y_test_scores = c.decision_function(X_test) print("Training Data:") evaluate_print(fname_alg, y_train_pred, y_train_scores) print("Test Data:") evaluate_print(fname_alg, y_test_pred, y_test_scores) visualize(fname_alg, X_train, y_train, X_test, y_test_pred, y_train_pred,y_test_pred, show_figure=True, save_figure=True) Anomaly Detection by Prophet import prophet from prophet import forecaster from prophet import Prophet m = Prophet() data = pd.read_csv('air_pass.csv') data.head() data.columns = ['ds', 'y'] data['y'] = np.where(data['y'] != 0, np.log(data['y']), 0)

        The Log of the y column enables no negative value. We split our data into train, test, and stored the prediction in the variable forecast.

        train, test= train_test_split(data, random_state =42)[['ds','y']]) forecast = m.predict(test) def detect(forecast): forcast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].copy() forcast['real']= data['y'] forcast['anomaly'] =0 forcast.loc[forcast['real']< forcast['yhat_lower'], 'anomaly']=-1 forcast['imp']=0 in_range = forcast['yhat_upper']-forcast['yhat_lower'] forcast.loc[forcast['anomaly']==1, 'imp'] = forcast['real']-forcast['yhat_upper']/in_range forcast.loc[forcast['anomaly']==-1, 'imp']= forcast['yhat_lower']-forcast['real']/in_range return forcast detect(forecast)

        We took the anomaly as -1.


        The process of finding outliers in a given dataset is called anomaly detection. Outliers are data objects that stand out from the rest of the object values in the dataset and don’t behave normally.

        Anomaly detection tasks can use distance-based and density-based clustering methods to identify outliers as a cluster.

        We here discuss anomaly detection’s various methods and explain them using the code on three datasets of Titanic, Air passengers, and Caret to

        Key Points

        1. Outliers or anomaly detection can be detected using the Box-Whisker method or by DBSCAN.

        2. Euclidean distance method is used with the items not correlated.

        3. Mahalanobis method is used with Multivariate outliers.

        4. All the values or points are not outliers. Some are noises that ought to be garbage. Outliers are valid data that need to be adjusted.

        5. We used PyCaret for outliers detection by using different algorithms where the anomaly is one, shown in yellow colors, and no outliers where the outlier is 0.

        6. We used PyOD, which is the Python Outlier detection library. It has more than 40 algorithms. Supervised and unsupervised techniques it is used.

        7. We used Prophet and defined the function detect to outline the outliers.

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        Learn The Powershell Command For Nslookup

        Introduction to powershell nslookup

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        Hadoop, Data Science, Statistics & others

        Powershell nslookupy overviews

        The powershell nslookup it’s the important one for the network management tools and it is the Bind name for accessing the software applications in the servers. Mainly it focuses on the Internet-based computing systems for planned to deprecate the host and other proxy modes, dig, etc. When we use nslookup it does not use the operating system for accessing the domains and it resolves in any type of server modes like both localhost and globally made access. If we use the domain name system, it has some set of rules and libraries for tunning and performing the queries it’s may operate with different manners and situations. For each vendor, its varied depends upon the requirement and versions provided from the system additionally it includes the output of the other data sources which related to the user information’s getting by the other input data contents which related to the configurations that may require by the user end. The Network Information Service(NIS) it’s the main source of the nslookup its related to the host files, sub-domains, and other proxy-related things. The nslookup may vary it depends upon the operating system and system requirements because of the network bandwidth and other related things like pinging the url for to checking the network data packets.

        Powershell command for NSLookup

        Generally, it has more than a single way to perform the domain name system like DNS query for achieving the nslookup commands to operate the tool and to fetch the DNS  record of each of the names specified with the domain resolver for Resolve-DnsName. When we perform this operation first it creates the empty array so it does not initialize the value then once the operation is triggered the each value will be stored as a separate memory allocation. If we use any programming loops iterating the values and the powershell will pass more focus on each data item including variables, operators, and even methods both default and customized methods. For each iteration of the loops it creates the temporary object and then it swaps to the original with the constant memory location. We can also utilize the nslookup command to resolve the ip to a hostname with commands like “nslookup %ipaddress%” in the screen for validating the datas to the powershell server for each and every session the object will be newly created once the session is closed or terminate it will automatically exist on that time.

        Use Nslookup in Powershell

        The nslookup command is equivalent power of the Domain name System Resolver it’s mostly configured and used in the cmdlet that can be performed using the DNS System the query cannot be fetched and it will not be retrieved from the particular domain name. We can use this in the powershell not only in that it is equivalent to the same and different DNS server. Because we can use the DNS with the specified and for network troubleshooting to identify the issue. We also would use the ping command to check the network connections and the host sites for checking, validate the datas with the specific IP address and which has performed the DNS reverse lookup for validating the datas it has configured and called the reverse proxy using fetch the query DNS and AD with the IP networks for a group of computers will join to the Active Directory Services in the domain for Active computers that already configured using the IP Address in the every User account of the computer system. Generally combining a group of hostnames for every IP address that can be done within the loop also iterates the datas. The recording datas are stored using the DNS record if any of the IP data is mismatched or not assign properly at that time it will throw an error like “IP not resolve” so that we can check the ip address of the specified system.

        DNS NsLookup in PowerShell

        PowerShell command for NSLookup

        Some of the PowerShell commands will use the hostname to finding the IP address with some parameters. Like that below,

        Based on the above commands we can retrieve data from the server.


        The nslookup is the command for getting the information from the Domain Name System(DNS) server with the help of networking tools using the Administrator rights. Basically, it obtains the domain name of the specific ip-address by using this tool we can identify the mapping of the other DNS records to troubleshoot the problems.

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        This is a guide to powershell nslookup. Here we discuss the Powershell command for NSLookup along with the overview. You may also have a look at the following articles to learn more –

        Learn The Two Main Concepts Ofsecurity

        Introduction to chúng tôi security

        Web development, programming languages, Software testing & others

        Authentication of chúng tôi security

        In chúng tôi there are many different types of authentication procedures for web applications. If you want to specify your own authentication methods, then that also is possible. The different modes are accepted through settings that can be applied to the application’s web.config file. The web.config file is XML based file which allows users to change the behavior of the chúng tôi easily. In chúng tôi there are three different authentication providers as windows authentication, Forms authentication, and passport authentication.

        1. Windows authentication

        This authentication provider is the default provider for chúng tôi It authenticates the users based on the user’s windows accounts. windows authentication relies on the IIS to do the authentication. IIS can be configured so that only users on the Windows domain can log in. If users attempt to access a page and is not authenticated, then the user will be shown a dialogue box that asks the user to enter their username and password. Then this information is passed to the webserver and checked against the list of users in the domain. Based on the result the access is granted to the user.

        To use windows authentication, the code is as follows

        there are four options in windows authentication that can be configured in IIS

        Basic authentication: In this, windows user name and password have to be provided to connect. This information is sent over the network in plain text and hence this is an insecure kind of authentication.

        Integrated windows authentication: In this, the password is not sent across the network and some protocols are used to authenticate users. It provides the tools for authentication and strong cryptography is used to help to secure information in systems across the entire network.

        Anonymous authentication: In this, IIS does not perform any authentication check and allows access to any user to the chúng tôi application.

        Digest authentication: It is almost the same and basic authentication but the password is hashed before it is sent across the network.

        2. Forms authentication

        It provides a way to handle the authentication using your own custom logic within the chúng tôi application. When the user requests a page for the application, chúng tôi checks for the presence of a special session cookie. If the cookie is present, chúng tôi assumes that the user is authenticated and processes the request. If the cookie is not present, chúng tôi redirects the user to a web form you provide. When the user is authenticated process the request and indicates this to chúng tôi by setting a property, which creates the special cookie to handle the subsequent requests.

        To use form authentication, the code is as follows

        3. Passport authentication

        To use passport authentication, the code is as follows

        Authorization of chúng tôi security

        Authentication and authorizing are two interconnected security concepts. Authorization is the process of checking whether the user has access to the resources they requested. In chúng tôi there is two forms of authorization available, one is file authorization and another is URL authorization.

        File authorization: File authorization is performed by the FileAuthorizationModule. It uses the ACL (Access Control List) of the .aspx to resolve whether a user should have access to the file. ACL permissions are confirmed of the user’s windows identity.

        Syntax is as follows

        This code will allow user SwatiTawde and deny all other users to access that application. If you want to give permission for more users then just add usernames separated with a comma like SwatiTawde, eduCBA, edu, etc. and if you want to allow only admin roles to access the application and deny permission for all the roles, then write the following code in web.config

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